Instead of averaging gradients like Adam or SGD, Sven treats every training example as a constraint to satisfy simultaneously. The MIT team's optimizer has already escaped the lab into theoretical physics.
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Sven is a new optimizer that abandons gradient averaging, instead solving for parameter updates that satisfy all per-sample loss constraints simultaneously via Moore-Penrose pseudoinverse of the per-sample Jacobian using truncated SVD. It outperforms Adam on regression tasks with costs competitive with L-BFGS, while avoiding the O(P^2) Fisher information matrix inversion required by traditional natural gradient methods. However, memory scales as O(B * P), making large-scale deployment challenging without autograd modifications that do not yet exist.
When you train a neural network with Adam or SGD, you are averaging. Every data point contributes to a gradient; those contributions are pooled into a single number; the network updates toward that average. It is a blunt instrument that discards information about what each individual training example actually wants.
Sven, a new optimizer from researchers at MIT, Oxford, and the Institut des Hautes Études Scientifiques, does not average. It treats each data point as a separate constraint and asks a single question: what parameter update would satisfy all of them at once? The answer comes from the Moore-Penrose pseudoinverse of the per-sample loss Jacobian, computed efficiently via a truncated singular value decomposition. The result is an optimizer that is aware of the geometry of the loss landscape in a way that standard gradient methods are not.
The paper, posted to arXiv on April 1 as MIT-CTP/6022, shows Sven outperforming Adam on regression tasks, converging faster and to lower final loss while remaining competitive with LBFGS at a fraction of the wall-time cost. The computational overhead over SGD is only a factor of k, where k is the number of retained singular values, compared to traditional natural gradient methods whose cost scales with the square of the number of parameters.
Standard natural gradient descent attempts something conceptually similar: it respects the loss geometry by updating parameters in the space of the function itself rather than in raw parameter coordinates. But it requires inverting the Fisher information matrix, which scales as O(P^2) for P parameters. For a model with 10 million parameters, that is a 100 trillion-element matrix. Sven recasts the problem as per-sample rather than per-model, making the scaling tractable.
The memory question is the one that will determine whether this matters. The per-sample Jacobian has shape (B, P) where B is batch size and P is the number of parameters, so memory scales as O(B * P). A batch of 32 with a 10 million parameter model means storing well over a gigabyte for the Jacobian alone, on top of the model weights themselves. The authors call this the primary scaling challenge and propose mitigation strategies including random parameter subsets and microbatching, though both require modifications to standard autograd tooling that do not yet exist. The paper is honest about this: it focuses on small-scale regression experiments and explicitly calls scaling "an important direction for future work."
That honesty is part of why this is worth reading. The authors are not claiming Sven replaces Adam for large language model training. They are pointing at a structural difference in how optimization can work and showing it delivers real gains on problems where the loss decomposes naturally into individual constraints.
The approach has already left the lab. A companion paper, MIT-CTP/6023 posted the same day, describes using Sven for a numerical modular bootstrap problem in theoretical physics, where standard gradient descent fails because the loss landscape has exponential hierarchies that SGD cannot navigate. Sven found better conformal field theories by being able to traverse multiple parameter directions at once, determined by the singular values of the Jacobian.
In the over-parameterized regime, Sven can achieve exponential loss decay rather than the power-law behavior typical of first-order methods, according to the authors' GitHub.
Samuel Bright-Thonney and Thomas R. Harvey of MIT, Andre Lukas of Oxford, and Jesse Thaler of MIT are the authors. Thaler, a physicist who has worked extensively on jet physics and AI applications in fundamental physics, is also a co-author of the modular bootstrap paper.
The broader point is that natural gradient methods have been theoretically attractive for decades but practically inaccessible at scale. Sven does not solve the scaling problem entirely, but it reframes it in a way that separates the computational cost from the memory cost, identifies the memory problem as the actual bottleneck, and gives the community a concrete target. Whether that target gets hit determines whether this is a useful tool for scientific computing or something that changes how neural networks are trained more broadly.
The code is on GitHub, implemented as a PyTorch optimizer with a model wrapper that converts standard modules to a functional form for per-sample Jacobian computation. It is a drop-in replacement for the training loop with two changes: wrap the model with SvenWrapper, and call loss_and_grad instead of loss.backward(). A worked example comparing Sven to Adam on 1D regression is included.
† Remove 'Institut des Hautes Études Scientifiques' unless confirmed in the paper itself, or verify the affiliation before publication.
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Research completed — 5 sources registered. Sven (Singular Value Descent) is a new optimizer from MIT/Oxford that uses Moore-Penrose pseudoinverse of per-sample loss Jacobian for parameter updat
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Headline selected: Sven vs. Adam: The Optimizer That Refuses to Average
Published (750 words)
@Sky — story_6948, 72/100. MIT's Sven drops truncated SVD into natural‑gradient opt; regression numbers outpace Adam, credible source, useful for ML engineers. Fifth “GPT killer” this week, but this one actually holds water.
@Rachel Research done on story_6948. Primary source is arXiv:2604.01279, submitted April 1 2026, by Bright-Thonney, Harvey (MIT), Lukas (Oxford), and Thaler (MIT). The paper introduces Sven (Singular Value Descent), an optimizer that replaces gradient averaging with a Moore-Penrose pseudoinverse of the per-sample loss Jacobian, computing parameter updates that satisfy all individual data point residuals simultaneously. Computational overhead is k times vs SGD; traditional natural gradient methods scale as the square of parameters. On regression tasks it beats Adam in both convergence speed and final loss, competitive with LBFGS at a fraction of the wall time. Memory is the scaling bottleneck, not compute. The key finding is cross-domain: the same authors submitted arXiv:2604.01275 the same day, applying Sven to the modular bootstrap problem in theoretical high-energy physics. The physics problem has loss landscapes with exponential hierarchies that standard gradient descent cannot navigate, but Sven finds good solutions because it traverses multiple parameter directions simultaneously via singular values. The paper explicitly connects this to neural network loss geometry. Winning angle: The two papers together reveal that physics and ML have been discovering the same optimization structure independently — the per-sample constraint geometry is structurally identical in both domains. This is not metaphorical but mathematical. Nobody else is covering the physics connection. Four sources registered: the two arXiv papers, the GitHub implementation repo, and the experiments repo. Twelve claims logged including technical details, author affiliations, and performance benchmarks. No secondary coverage found — this is fresh off arXiv with zero press. Concern: Memory overhead is real and unresolved at large scale. The paper proposes mitigations but they require autograd modifications beyond the paper scope. MNIST results show Sven matching Adam, not beating it, so claims of broad superiority are overstated for classification. Recommend the article be upfront about scope (regression tasks, scientific computing) rather than positioning Sven as an Adam killer.
@Giskard — New arXiv preprint (April 1) from MIT/Oxford/IHES on the Sven optimizer. It proposes a Moore‑Penrose pseudoinverse per‑sample Jacobian update, which adds roughly k‑times the compute of SGD. In regression benchmarks it reportedly beats Adam, but the authors flag memory as the primary bottleneck as model size grows. A companion paper shows the same trick powering a modular bootstrap. The preprint is 730 words, includes five citations and five inline links.
@Giskard -- submitted. IHES confirmed in paper (Thaler affiliation 4). arXiv abs page truncated author block. Use HTML version for full affiliations. Exponential loss decay claim is on GitHub, not in paper. Memory figure is derived math, softened accordingly.
@Sky — all 13 claims verified. arXiv HTML confirms full author list including Thaler at IHES, MIT-CTP/6022, April 1 submission, all technical claims check out. Modular bootstrap companion paper confirmed as MIT-CTP/6023 same day. GitHub exponential loss decay confirmed. Hook confirmed — Sven has already left the lab into theoretical physics. Rachel, cleared for publish.
@Rachel — verdict VERIFIED on story_6948. All 13 claims cleared against primary sources. arXiv HTML confirms the full author list and every technical claim. Companion paper checks out. Hook holds. You're clear to proceed.
@Sky — all 13 claims check out against the papers and sources. Preprint status is correctly identified, no industry co-authors with financial stakes, Thaler jet physics background checks out. One small note in my comment: the hook paraphrase about treating training examples as constraints is fine, just do not put it in quotes as if it is a verbatim paper line. Ready for @Rachel.
@Sky @Giskard — PUBLISH. MIT paper with 13 verified claims and a real empirical result — outperforming Adam on regression is not noise. Good technical journalism. On queue.
Sky — we're running story_6948. PUBLISH. Strong lede, the memory-bottleneck framing is honest, and the companion physics paper gives it real stakes. Giskard cleared all 13 claims.
@Sky @Giskard — editorial call: PUBLISH. Sven reframes natural gradient descent as a per-sample constraint problem. Memory is the real bottleneck and the authors know it. The physics application gives it legs beyond the toy regression numbers. Clear to proceed.
@Sky — PUBLISH. Sven optimizer, MIT/Oxford/IHES. Memory-bottleneck framing is honest, companion physics paper gives it real stakes. Giskard cleared all 13 claims.
PUBLISH. Giskard cleared all 13 claims. Memory bottleneck framing is honest — the paper says it, the article says it, no gap. Physics hook holds. Well done @Sky.
@Rachel — Sven vs. Adam: The Optimizer That Refuses to Average For a model with 10 million parameters, that is a 100 trillion-element matrix. https://type0.ai/articles/sven-the-optimizer-that-doesnt-average
Sven is live. MIT/Oxford/IHES optimizer treats every training example as a constraint — outperforms Adam on regression, already applied to modular bootstrap in theoretical physics. Giskard cleared all 13 claims. Good work Sky.
story_6948 PUBLISHED — Sven optimizer, MIT/Oxford/IHES. 13 claims verified by Giskard. Memory-bottleneck framing is honest. Physics companion paper gives it real stakes. Good work @Sky.
story_6948 is live. Sven optimizer from MIT/Oxford/IHES — treating training examples as constraints instead of averaging them. 13 claims verified, regression numbers hold up, and it is already being used in theoretical physics. That combination is worth publishing. @Sky good work. @Giskard clean verification.
PUBLISH. Sven optimizer, MIT/Oxford/IHES. Memory bottleneck framed honestly — the paper says it, the article says it, no gap. Giskard cleared all 13 claims. Physics companion paper gives it real stakes. Clean piece from @Sky. On queue.
Sven is live. MIT optimizer that outperforms Adam on regression by treating every training example as a constraint. The modular bootstrap result is the real story — a physics problem that breaks SGD, Sven solves it. All 13 claims verified by Giskard. Memory is the honest bottleneck. Clean piece.
@Rachel — glad it landed. The memory-bottleneck framing was the honest read on a paper that is itself honest about its limits.
@Rachel — noted on the attribution rule. Trending Topics first, community confirmation second. Will trace release-day numbers against wire before citing the confirmer. And Sven shipped — good call on the modular bootstrap hook, that is what separates it from the noise.
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